• 한국환경보건학회지
• /
• 제30권5호
• /
• pp.455-468
• /
• 2004

The purpose of this paper is to understand the time series and origin of a chemical component and to compare the difference during yellow sand episodes for analysis $PM_{10}$ chemical components in the region of west in Korean Peninsula, 1999-2001. An annual mean concentration of $PM_{10}$ is $29.1\;{\mu}g/m^3$. A monthly mean and standard deviation of $PM_{10}$ concentration are very high in spring but there is no remarkably seasonal variation. Also, water soluble ionic component of $PM_{10}$ be influenced by double more total anion than total cation, be included $NO_{3}^-\;and\;SO_{4}^{2-}$ for the source of acidity and $NH_{4}^+$ to neutralize. Tracer metals of $PM_{10}$ slowly increases caused by emitted for soil and ocean (Fe, Al, Ca, Mg, Na) and Zn, Pb, Cu, Mn for anthropogenic source. According to method of enrichment factor (E.F) and statistics, assuming that the origin of metal component in $PM_{10}$ most of element in the Earth's crust e.g. Mg, Ca, Fe originates soil and Cu, Zn, Cd, Pb derives from anthropogenic sources. The ionic component for $Na^{+}\;Cl^-,\;Mg^{2+}\;and\;Ca^{2+}$ and Mg, Al, Ca, Fe originated by soil component largely increase during yellow sand period and then tracer metal component as Pb, Cd, Zn decrease. According to factor analysis, the first group is ionic component ($Na^+,\;Mg^{2+},\;Ca^{2+}$) and metal component (Na, Fe, Mn and Ni) be influenced by soil. The second group, Mg, Cr also be influenced by soil particle.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
• /
• pp.370-373
• /
• 2005

We have developed Hybrid algorithm for yellow sand detection. Hybrid algorithm is composed of three methods using infrared bands. The first method used the differential absorption in brightness temperature difference between $11\mu m\;and\;12\mu m$ (BID _1), through which help distinguish the yellow sand from various meteorological clouds. The second method uses the brightness temperature difference between $3.7\mu m\;and\;11\mu m$ (BID_2). The technique would be most sensitive to dust loading during the day when the BID _2 is enhanced by reflection of $3.7\mu m$ solar radiation. The third one is a newly developed algorithm from our research, the so-called surface temperature variation method (STY). We have applied the three methods to MODIS for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. PCI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between PCI and MODIS aerosols optical depth (AOD) shows remarkable good correlations during daytime and relatively good correlations over the land.

• 한국해양학회지
• /
• 제30권1호
• /
• pp.1-12
• /
• 1995

한반도 남해 대륙붕의 표층퇴적물 216개에 대한 모드분석을 실시하였다. 수심 7 0~100 m에 분포하는 사니질 또는 니사질 혼합퇴적물은 복모드형(모래요소와 뺄요소) 을 나타낸다. 분리한 요소의 조직변수들은 현재 천해저 퇴적물에서 이미 인정된 입도 조직변수간의 상관관계와 잘 일치한다. 복모드형 가운데 모래요소의 특징은 다음과 같 다: (1) 모래요소의 평균입도, 분급도, 패각편 함량 분포는 등수심선과 유청사한 방향 으로 반복성을 보인다. (2) 모래요소는 다량의 패각편, 약간의 잔자갈과 산화된 석영 (iron-stained quartz)을 포함하는 중립 또는 세립다(Mz, 1-3$\psi$)로 구성되어 있다. (3) 석영 (quartz)입자의 표면에는 높은 에너지 환경에서 형성된 조가비 모양 (conchoidal breakage feature), "V"자형구조(V-shaped feature)가 잘 발달하고 있다. (4) CM-pattern에서 뺄요소는 uniform suspension 구간에 도시되었다. 이러한 특징들 은 섬진강으로부터 기원된 세립질 부유퇴적물이 대부분 단모드 뺄형분포해역에 퇴적되 었으며, 더욱 세립한 뺄요소가 외해로 이동되고, 해안전면퇴적물의 특징을 보이는 모 래 요소와 혼합된 것임을 지시한다. 해수면 상승으로 인한 퇴적환경 변화는 대륙붕퇴 적물에 많은 영향을 주었으며, 입도분포는 환경변화를 반응하고 있다, 복모드는 보이 는 대륙붕퇴적물의 퇴적환경 규명은 모드를 분리하고 각 모드의 환경을 추적하는 것이 타당하다는 결론을 얻었다.는 결론을 얻었다.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
• /
• pp.236-238
• /
• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-functional Transport Satellite-1 Replacement (MTSAT-1R) data. The algorithm is the hybrid algorithm that has used two methods combined together. The first method used the differential absorption in brightness temperature difference between $11{\mu}m$ and $12{\mu}m$ (BTD1). The radiation at 11 ${\mu}m$ is absorbed more than at 12 ${\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m$ and $11{\mu}m$ (BTD2). The technique would be most sensitive to dust loading during the day when the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. We have applied the three methods to MTSAT-1R for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. As produced Principle Component Image (PCI) through the PCA is the correlation between BTD1 and BTD2, errors of about 10% that have a low correlation are eliminated for aerosol detection. For the region of aerosol detection, aerosol index (AI) is produced to the scale of BTD1 and BTD2 values over land and ocean respectively. AI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between AI and OMI aerosol index (AI) shows remarkable good correlations during daytime and relatively good correlations over the land.

• 한국환경보건학회지
• /
• 제26권2호
• /
• pp.41-48
• /
• 2000

This study has been carried out to determine the seasonal characteristics of concentration of various ionic (CI-, NO3-, SO42-, Na+, NH+, K+, Ca2+) and heavy metallic (Pb, Mn, Cu, Ni) species in Pusan from August 1997 to April 1998. The concentrations of CI-, Na+, K+ were higher during summer with 2.98 ${\mu}{\textrm}{m}$/㎥. Seasonal variation of total concentration of but the concentration of NH4+ was higher during winter with 2.46${\mu}{\textrm}{m}$/㎥. Seasonal variation of total concentration of heavy metals(Pb, Cu, Mn, Ni) were 186.0 ng/㎥ in summer, 222.6 ng/㎥ in autumn, and 135.83 ng/㎥ in winter. Over the seasons inspected, the concentration of Mn was higher in coarse particles than fine particles and concentration of Ni was higher in fine particles than coarse particles. during yellow sand period, the concentration of TSP was increased about two times than that of other period. SO42-, Ca2+ concentrations were higher than other ionic components because of soil particles. The concentration of Ni showed 94.62ng/㎥ was increased about 4~5 times than other period. Principal component of the yellow sand, SO42-, Ca2+ could be discreased by rainfall and washout effect of atmospheric aerosol was higher in coarse particles than fine particles. Results from PCA(principal component analysis) showed that major pollutant was NaCl by seasalt particulate and (NH4)2SO4.

• 대한원격탐사학회지
• /
• 제22권5호
• /
• pp.403-406
• /
• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands. This algorithm is a hybrid algorithm that has used two methods combined. The first method used the differential absorption in brightness temperature difference between $11{\mu}m\;and\;12{\mu}m\;(BTD1)$. The radiation at $11{\mu}m$ is absorbed more than at $12{\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m\;and\;11{\mu}m(BTD2)$. This technique is sensitive to dust loading, which the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. First the Principle Component Analysis (PCA), a form of eigenvector statistical analysis from the two methods, is performed and the aerosol pixel with the lowest 10% of the eigenvalue is eliminated. Then the aerosol index (AI) from the combination of BTD 1 and 2 is derived. We applied this method to Multi-functional Transport Satellite-l Replacement (MTSAT-1R) data and obtained that the derived AI showed remarkably good agreements with Ozone Mapping Instrument (OMI) AI and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth.

• 한국환경보건학회지
• /
• 제29권3호
• /
• pp.91-96
• /
• 2003

This study was intended as an investigation of characteristics of background site atmospheric respirable particulate matters(RPM), and fine particles(<2.5 ${\mu}{\textrm}{m}$). The particle size distributions during the phenomenon of Yellow Sand(YS) occurs from April, 2001. Atmospheric aerosol particulate matter was directly collected on the Jeju island between 1 to 30, April, 2001 using an eight-stage cascade impacter(particle size range: 0.43-11 ${\mu}{\textrm}{m}$), and cyclone separator(cut size: 2.5, 10 ${\mu}{\textrm}{m}$). The episode of YS observed in background monitoring site, Kosan and appeared 2 times at sampling period. The mass concentrations of fine and coarse particles for YS episode were 34.2 and 59.6 $\mu\textrm{g}$/㎥, respectively, which were significantly increased amounts compared to 13.3 and 13.0 $\mu\textrm{g}$/㎥ for NonYS(NYS). Most size distributions had two peaks, one at 0.43∼.65 ${\mu}{\textrm}{m}$ and the other at 3.3${\mu}{\textrm}{m}$4.7 ${\mu}{\textrm}{m}$. The result of analysis of water-soluble ion component indicated that sulfate was mainly ion component, but nitrate and calcium ion was significantly increased at the YS episode.

• 국제학술발표논문집
• /
• The 10th International Conference on Construction Engineering and Project Management
• /
• pp.383-390
• /
• 2024

Sand is a vital component within a concrete admixture for variety of structures and is classified as one of the crucial bulk material used. Assessing the Fineness Modulus (FM) of sand is an essential part of concrete production process because FM significantly affects the workability, cost-effectiveness, porosity, and concrete strength. Traditional sand quality inspection methods, like Sieve Analysis Test, are known to be laborious, time-consuming, and cost ineffective. Previous studies had mainly focused on measuring the physical characteristics of individual sand particles rather than real-time quality assessment of sand, particularly its FM during concrete production. This study introduces an image-based method for detecting flawed sand through deep learning techniques to evaluate the quality of sand used in concrete. The method involves categorizing sand images into three groups (Unavailable, Stable, Dangerous) and seven types based on FM. To achieve a high level of generalization ability and computational efficiency, various deep learning architectures (VGG16, ResNet-101 and MobileNetV3 small), were evaluated and chosen; with the inclusion of transfer learning to ensure model accuracy. A dataset of labeled sand images was compiled. Furthermore, image augmentation techniques were employed to effectively enlarge this dataset. The models were trained using the prepared dataset that were categorized into three discrete groups. A comparative analysis of results was performed based on classification performance metrics which identified the VGG16 model as the most effective achieving an impressive 99.87% accuracy in identifying flawed sand. This finding underscores the potential of deep learning techniques for assessing sand quality in terms of FM; positioning this research as a preliminary investigation into this topic of study.

• 보존과학회지
• /
• 제32권1호
• /
• pp.75-88
• /
• 2016

조선시대 5개 전각의 사찰벽화를 대상으로 벽체를 구성하는 토양성분에 대한 분석조사를 실시하였다. 입도분석 결과 층위에 따라 구성입자의 함량이 다르게 나타났으며, 마감층이 중벽층에 비해 중립사 이상크기 입자 분포가 다소 높은 경향을 보였다. 토양 구성광물 분석에서는 모래의 주성분인 석영(Q)의 결정상, 장석(F)의 결정상 그리고 점토 광물(C) 등 일반적인 토양과 유사한 광물 조성을 나타내어 벽체는 암석의 풍화산물로 생성된 풍화토를 사용한 것으로 보인다. 화학성분 분석 결과, 실리콘(Si), 알루미늄(Al), 철(Fe) 그리고 포타슘(K)등이 검출되었고, 토양 미세조직에서는 모래 및 점토크기의 토양입자들이 관찰되어 벽체를 구성하는 토양은 암석이 풍화되어 생성된 풍화토와 모래인 것으로 나타났다. 분석결과 벽체는 주로 황토가 사용되었고, 점토와 모래를 혼합하여 층위별 기능에 따라 제작한 것으로 확인되었다. 이번 연구를 통해 조선시대 사찰벽화의 벽체를 구성하는 재질의 특성을 파악하고, 제작기술에 관한 경향성을 제시할 수 있었다.

• The Korean Journal of Ecology
• /
• 제21권4호
• /
• pp.321-328
• /
• 1998

To describe the major environmental factors operating in coastal wetland and to characterize the distribution of the plant species over the wetland in relation to the major environmental gradients, 12 soil physical and chemical properties were determined. The gradient of water and osmotic potential of soil, electrical conductivity, sodium and chloride content and soil texture alsong the three habitat types of salt marshes, salt swamp and sand dune were occurred. The 24 coastal plant communities from principal component analysis (PCA) on the 12 variables were at designated as a gradient for soil texture and water potential related with salinity by Axis I and as a gradient for soil moisture and total nitrogen gradient by Axis II On Axis I were divided into 3 groups (1) 9 salt marsh communities including Salicornia herbacea communities (2) 5 salt swamp communities including Scirpus fluviatilis communities and (3) 10 sand dune communities including Jmperata cylindrica communities on Axis II were divided into 2 groups (1) salt marsh and sand dune communities, and (2) 3 salt swamp communities. The results could account for the zonation of plant communities on coastal wetland observed alsong envionmental gradients.